Good points, but I'd push back just a little. > Sure, the actual implementations can be tricky, but a single engineer (trained in it) can get that done for a s

Good points, but I'd push back just a little. > Sure, the actual implementations can be tricky, but a single engineer (trained in it) can get that done for a specific hardware in a couple months. I want to agree with you on this, but in practice, it's... 1. Hard to hire that engineer with deep expertise in handwritten kernels. CUDA engineers are still hard to come by and doesn't scale with productionized AI engineering demand. 2. "A few months" is a tough pill to swallow from an engineering roadmap POV, especially when models are deployed on a monthly basis. Most of the hand tuning efforts aren't scalable and will have to be done again on most iterations. This is especially true in reinforcement learning and robotics. > But, in retrospect, the actual value add from a system that does all this for you is quite low. It's a realization I've been struggling to accept. Yeah I remain neutral on this. On one hand, I can see that especially having to invest significant engineering effort (see point 2 above). On the other hand, you won't really know until you start benchmarking these models (and as you should).